A Comparative Proteomic Analysis to Explore the Influencing Factors on Endometritis Using LC-MS/MS

The inflammatory system activated by uterine infection is associated with decreased fertility. Diseases can be detected in advance by identifying biomarkers of several uterine diseases. Escherichia coli is one of the most frequent bacteria that is involved in pathogenic processes in dairy goats. The purpose of this study was to investigate the effect of endotoxin on protein expression in goat endometrial epithelial cells. In this study, the LC–MS/MS approach was employed to investigate the proteome profile of goat endometrial epithelial cells. A total of 1180 proteins were identified in the goat Endometrial Epithelial Cells and LPS-treated goat Endometrial Epithelial Cell groups, of which, 313 differentially expressed proteins were accurately screened. The proteomic results were independently verified by WB, TEM and IF techniques, and the same conclusion was obtained. To conclude, this model is suitable for the further study of infertility caused by endometrial damage caused by endotoxin. These findings may provide useful information for the prevention and treatment of endometritis.


Introduction
Uterine diseases, such as hysteritis and endometritis, are common in high-yield dairy goats and lead to economic losses due to a decline in milk production and fecundity [1,2]. Gram-negative bacteria are usually associated with uterine infection in dairy goats [2,3]. Dystocia and placental retention of dairy goats make them vulnerable to uterine diseases because the physical barrier of infection is broken and the immune response is disrupted, so pathogens should be eliminated [2,3].
Successful implantation requires a balance and accurate molecular communication between the embryo and the maternal endometrium [3,4]. Even a small imbalance could negatively affect the dialog between the mother and the embryo necessary for the establishment of pregnancy [4].
According to the diagnosis of endometrial cytology, the prevalence of endometritis in dairy cows is very high [4,5], which increases the reproductive cycle and time costs [5]. Several studies have confirmed that the incidence of endometritis 35-45 days after delivery is very high [5,6], resulting in a significant reduction in the fertility rate of the population [5,6]. Although consistently contaminated or infected in the early postpartum period [6], it is essentially sterile by approximately 28 days postpartum [5,6]. Finally, the characteristics of the relationship between uterine infection and endometritis can reveal the mechanism by which endometritis prolongs pregnancy intervals and even leads to infertility [7,8].

Identification and Hierarchical Clustering Heatmap
We identified a total of 1180 proteins in the goat Endometrial Epithelial Cells (gEECs) and LPS-treated goat Endometrial Epithelial Cell groups. Among them, 1097 proteins were common in both groups, and 21 and 62 proteins were unique to the gEECs and LPS-treated gEEC groups, respectively ( Figure 1I). At the same time, 313 differentially expressed proteins were screened by p-value (Supplementary File S1). For the gEECs and LPS-treated gEEC groups, protein clustering can be seen, as in Figure 1II. The hierarchical clustering analysis of the two groups showed that F1 and F3 and F8 and F9 were clustered together at first, followed by F2 and F7. The red color showed the up-regulated expression, and the blue color represented the down-regulated expression. The color from red/blue to white represented the ratio from large to small. The cluster analysis of different proteins shows that the cluster map can be divided into several parts. The sensitivity of these proteins to inflammation is particularly important. The proteins clustered together indicate a consistent response to inflammation, see Supplementary File S1.
Mol. Sci. 2023, 24, x FOR PEER REVIEW 2 of 20 period [6], it is essentially sterile by approximately 28 days postpartum [5,6]. Finally, the characteristics of the relationship between uterine infection and endometritis can reveal the mechanism by which endometritis prolongs pregnancy intervals and even leads to infertility [7,8].

Gene Ontology (GO) Functional Category Analysis
When analyzing differentially expressed proteins, there were significant differences in biological process ( Figure 2I), such as the single-organism metabolic process, singleorganism process and so on. Although they were insignificant, there are still differences in differentially expressed proteins, such as the regulation of membrane potential, regulation of protein targeting, response to estrogen, necroptotic process, maturation of LSU-rRNA, response to oxidative stress, organophosphate biosynthetic process, mitophagy, mitochondrion disassembly and so on.
When analyzing differentially expressed proteins, there were significant differences in cellular components ( Figure 2II), such as the extracellular vesicles, extracellular organelles, cytoplasmic components, membrane-bound organelles, extracellular membrane-bound organelles, membrane-bound vesicles, extracellular region components, intracellular organelles, intracellular membrane-bound organelles and so on. Although they were insignificant, there are still differences in differentially expressed proteins, such as the nascent polypeptide-associated complex, endoplasmic reticulum-Golgi intermediate compartment, late endosome membrane, early endosome, lytic vacuole membrane, eukaryotic translation elongation factor 1 complex, Cul7-RING ubiquitin ligase complex, lysosomal membrane, late endosome, Golgi membrane, COP9 signalosome, plasma membrane region, vacuolar components, recycling endosome membrane, lysosomal lumen and so on.
When analyzing differentially expressed proteins, there were significant differences in molecular function ( Figure 2III), such as peptidase regulator activity, endopeptidase regulator activity, oxidoreductase activity, catalytic activity, hydrogen, transmembrane movement of substances, ATPase coupled, anion binding, protein complex binding, hydrogen-exporting ATPase activity, nucleotide binding, nucleoside phosphate binding, ATPase activity, coupling, NAD(P)H oxidoreductase activity, NADH dehydrogenase activity and so on. Although they were insignificant, there are still differences in differentially expressed proteins, such as, helicase activity; quinone or similar compounds as acceptors; iron-responsive element binding; glucosylceramidase activity; 6-phosphofructokinase activity; translation release factor activity; codon specific, calmodulin-dependent protein phosphatase activity; beta-N-acetylhexosaminidase activity; sequence-specific mRNA binding; transferase activity; transferring of aldehyde and ketonic groups and so on.

Pathway Analysis
We performed Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis on differentially expressed proteins (DEPs), as shown in Figure 3. We obtained a total of 199 KEGG pathways, see Supplementary File S2.
When comparing the the gEECs and LPS-treated gEEC groups, metabolic pathways, carbon metabolism pathways, oxidative phosphorylation pathways, citrate cycle (TCA cycle) pathways, and endocytosis pathways were significantly different. Phagosome pathways were also significantly different. The lysine degradation pathway, one carbon pool by folate pathway, 2-oxocarboxylic acid metabolism pathway, purine metabolism pathway, glucagon signaling pathway and so on were different as well, but the difference was not significant.

PPI Network and Module Analysis of the Identified Proteins
We built PPI networks of proteins identified in the gEECs and LPS-treated gEEC groups.
All significantly different proteins in the gEECs and LPS-treated gEEC groups were analyzed by PPI. All 313 differentially expressed proteins were included in the protein interaction network of up-and down-regulated proteins following LPS stimulation of goat Endometrial Epithelial Cells ( Figure 4I). Among of them were 218 up-regulated proteins ( Figure 4II) and 95 down-regulated proteins ( Figure 4III). The PPI diagram shows that many kinds of differential proteins are related to each other, and the protein-protein interaction (PPI) network is highly complex. Network nodes represent proteins, while edges represent protein-protein associations which were already known (light blue and purple), or predicted (other colors) by String analysis.
From the PPI map, we can see interactions between the OLA1 and A2M; FETUB and SERPINF2; ALB and VCAN, TF and CALU, and TF and RBP4 proteins, and so on. From the PPI map, we can see interactions between the PSMB6 and UCHL5, PSMB6 and NDUFA2, ETFB and ETFA, HIBADH and ALDH4A1, and HIBADH and ABHD14B proteins, and so on. From the PPI map, we can see interactions between the CBR1 and AKR1A1, CBR1 and SPR; CBR1 and GART, DDX47 and PUS7, DDX47 and NOP56, PSMA3 and PSMA2, and PSMA3 and PSMB1 proteins, see Supplementary Files S1 and S2.

PPI Network and Module Analysis of the Identified Proteins
We built PPI networks of proteins identified in the gEECs and LPS-treated gEEC groups. All significantly different proteins in the gEECs and LPS-treated gEEC groups were analyzed by PPI. All 313 differentially expressed proteins were included in the protein interaction network of up-and down-regulated proteins following LPS stimulation of goat Endometrial Epithelial Cells ( Figure 4I). Among of them were 218 up-regulated proteins ( Figure 4II) and 95 down-regulated proteins ( Figure 4III). The PPI diagram shows that many kinds of differential proteins are related to each other, and the protein-protein interaction (PPI) network is highly complex. Network nodes represent proteins, while edges represent protein-protein associations which were already known (light blue and purple), or predicted (other colors) by String analysis.
From the PPI map, we can see interactions between the OLA1 and A2M; FETUB and SERPINF2; ALB and VCAN, TF and CALU, and TF and RBP4 proteins, and so on. From the PPI map, we can see interactions between the PSMB6 and UCHL5, PSMB6 and NDUFA2, ETFB and ETFA, HIBADH and ALDH4A1, and HIBADH and ABHD14B proteins, and so on. From the PPI map, we can see interactions between the CBR1 and AKR1A1, CBR1 and SPR; CBR1 and GART, DDX47 and PUS7, DDX47 and NOP56, PSMA3 and PSMA2, and PSMA3 and PSMB1 proteins, see Supplementary Files S1 and S2.  Protein-protein interactions (Evidence Mode) of dysregulated proteins were predicted by STRING. Each circle node represents 1 protein, the connection between node and node represents the interaction between 2 proteins, and lines with different colors indicate different interaction types. Colored nodes: query proteins and first shell of interactors, white nodes: second shell of interactors, filled nodes: some 3D structure is known or predicted. The network nodes represent proteins from the goat database, and the lines are the predicted functional annotations (red line-the presence of fusion evidence; green line-neighborhood evidence; blue line-cooccurrence evidence; purple line-experimental evidence; yellow line-text mining evidence; light-blue linedatabase evidence; black line-co-expression evidence).

Transmission Electron Microscopy (TEM)
KEGG analysis showed that the lysosome, proteasome and endocytosis pathways were significantly different between the gEECs and LPS-treated gEEC groups. The TEM results clearly show that the number of autophagic vesicles formed by late lysosomes in the LPS-treated gEECs group is much greater than that in the gEECs group ( Figure 5).
The results of proteomics were consistent with those of transmission electron microscopy. This means that lysosomes, autophagosomes and carbon metabolism are more active in LPS-treated gEECs than gEECs. Protein-protein interactions (Evidence Mode) of dysregulated proteins were predicted by STRING. Each circle node represents 1 protein, the connection between node and node represents the interaction between 2 proteins, and lines with different colors indicate different interaction types. Colored nodes: query proteins and first shell of interactors, white nodes: second shell of interactors, filled nodes: some 3D structure is known or predicted. The network nodes represent proteins from the goat database, and the lines are the predicted functional annotations (red line-the presence of fusion evidence; green line-neighborhood evidence; blue line-cooccurrence evidence; purple line-experimental evidence; yellow line-text mining evidence; light-blue line-database evidence; black line-co-expression evidence).

Transmission Electron Microscopy (TEM)
KEGG analysis showed that the lysosome, proteasome and endocytosis pathways were significantly different between the gEECs and LPS-treated gEEC groups. The TEM results clearly show that the number of autophagic vesicles formed by late lysosomes in the LPS-treated gEECs group is much greater than that in the gEECs group ( Figure 5).

qRT-PCR Analysis
Real-time PCR was performed to examine differences in the expression of inflammatory factor in LPS-mediated gEECs inflammatory response. Significantly more IL-1β and IL-6 mRNA was present in the LPS stimulation group compared to the control group (Figure 6). The change in inflammatory variables was conspicuous, proving that LPS were successful in making gEECs become inflammatory. The results of proteomics were consistent with those of transmission electron microscopy. This means that lysosomes, autophagosomes and carbon metabolism are more active in LPS-treated gEECs than gEECs.

qRT-PCR Analysis
Real-time PCR was performed to examine differences in the expression of inflammatory factor in LPS-mediated gEECs inflammatory response. Significantly more IL-1β and IL-6 mRNA was present in the LPS stimulation group compared to the control group ( Figure 6). The change in inflammatory variables was conspicuous, proving that LPS were successful in making gEECs become inflammatory.

Western Blot
To validate the DEPs identified from the label-free quantitation analysis, prot with important physiological functions were selected for detection. COL1A1 (Figure is involved in the structural components of extracellular matrix, platelet-derived gro factor binding, vascular development, the cell response to mechanical stimulation, the lagen-activated tyrosine-kinase receptor signal pathway, the collagen biosynthesis, p tive regulation of cell migration, positive regulation of epithelial-mesenchymal tran mation, and binding of platelet-derived growth factor. COL1A1 is a type I collage binds calcium ions, which are necessary for its function. The p-AKT ( Figure 7IV) h significant impact on cell survival and growth. Survival factors can suppress apoptos a transcription-independent manner by activating the serine/threonine kinase AK which then phosphorylates and inactivates components of the apoptotic machinery. S INE1 (PAI, Figure 7III) inhibits TMPRSS7. As a PLAU inhibitor, it is involved in cell and replicative senescence. Equal amounts of proteins from the gEECs were detected antibodies, as shown in Figure 7. The results showed that the ratios of the selected prot were consistent with those obtained from the label-free quantitation analysis. In fu work, we will conduct an in-depth study of the above proteins.

Western Blot
To validate the DEPs identified from the label-free quantitation analysis, proteins with important physiological functions were selected for detection. COL1A1 ( Figure 7II) is involved in the structural components of extracellular matrix, platelet-derived growth factor binding, vascular development, the cell response to mechanical stimulation, the collagen-activated tyrosine-kinase receptor signal pathway, the collagen biosynthesis, positive regulation of cell migration, positive regulation of epithelial-mesenchymal transformation, and binding of platelet-derived growth factor. COL1A1 is a type I collagen. It binds calcium ions, which are necessary for its function. The p-AKT ( Figure 7IV) has a significant impact on cell survival and growth. Survival factors can suppress apoptosis in a transcription-independent manner by activating the serine/threonine kinase AKT1, which then phosphorylates and inactivates components of the apoptotic machinery. SERINE1 (PAI, Figure 7III) inhibits TMPRSS7. As a PLAU inhibitor, it is involved in cellular and replicative senescence. Equal amounts of proteins from the gEECs were detected with antibodies, as shown in Figure 7. The results showed that the ratios of the selected proteins were consistent with those obtained from the label-free quantitation analysis. In future work, we will conduct an in-depth study of the above proteins.

Immunofluorescence
From Figure 8, it can be seen that the protein localization of COL1A1 ( Figure 8I), SERPINE1 (PAI, (Figure 8II)) and KNG1 ( Figure 8III) is in the cytoplasm, which is consistent with the results of GO analysis. The differential expression of COL1A1 and SERPINE1 (PAI) and KNG1 indicates that the protein expression profile of the control group and the inflammation group has changed, and it also shows that the biological function of the cells has changed from a side point of view, which may be the main cause of pregnancy failure.

Immunofluorescence
From Figure 8, it can be seen that the protein localization of COL1A1 ( Figure  SERPINE1 (PAI, (Figure 8II)) and KNG1 ( Figure 8III) is in the cytoplasm, which is c sistent with the results of GO analysis. The differential expression of COL1A1 and S PINE1 (PAI) and KNG1 indicates that the protein expression profile of the control gro and the inflammation group has changed, and it also shows that the biological functio the cells has changed from a side point of view, which may be the main cause of pregna failure.

Proteome Quality Difference between gEECs and LPS Treated gEECs Groups
Compared with the gEECs group, the expression of Q6QAT4 was downregulated in the LPS-treated gEECs group. Q6QAT4 is involved in antigen delivery in the immune system. It is also involved in antigen processing and presentation of exogenous protein antigens via MHC class I b, which are TAP-dependent processes. It is closely related to the inflammatory response. RPLP2 expression was downregulated in the LPS-treated gEECs group. RPLP2 participates in the process of protein synthesis and extension and is a structural constituent of ribosomes. This may indicate that the protein synthesis process is abnormal.

The Possible Role of Distinct Proteins Based on GO
When comparing the gEECs group, PRKAR2A expression was downregulated in the LPS-treated gEECs group. PRKAR2A has cAMP-dependent protein-kinase regulator activity and is involved in cAMP and ubiquitin-protein-ligase binding. It participates in the regulation of cAMP-mediated signaling. Compared with the gEECs group, KNG1 gene expression was downregulated in the LPS-treated gEECs group. This study was successfully verified by the immunofluorescence experiment. KNG1 is a mediator of inflammation. It has cysteine-type endopeptidase inhibitor activity. It participates in the inflammatory response and negative regulation of blood coagulation. When comparing the gEECs group, PPT1 expression was upregulated in the LPS-treated gEECs group. PPT1 removes thioester-linked fatty acyl groups. This may indicate that inflammation is related to cellular fat metabolism [5].

The Possible Role of Significantly Different Proteins in Cell Migration, Adhesion and Implantation
When comparing the gEECs group, ITGA5 expression was downregulated in the LPStreated gEECs group. ITGA5:ITGB1 acts as the receptor of fibrin 1 (FBN1), playing the role of adhesion. When comparing the gEECs group, SERPINE1 expression was downregulated in the LPS-treated gEECs group. As a PLAT inhibitor, SERPINE1is required for fibrinolysis downregulation and is responsible for the controlled degradation of blood clots.

The Possible Role of Different Proteins in Epithelial-Mesenchymal Transformation and Polarization
When comparing the gEECs group, COL1A1 expression was downregulated in the LPS-treated gEECs group. COL1A1 is an extracellular matrix structural constituent. It participates in blood vessel development, the collagen-activated tyrosine kinase receptor signaling pathway. This is consistent with the study of endometrial protein and uterine fluid Wang C and Li T [2,8].

The Possible Role of Significantly Different Proteins in the Process of Apoptosis and the Cell Cycle
When comparing the gEECs group, MCM6 expression was downregulated in the LPStreated gEECs group. MCM6 acts as a component of the MCM2-7 complex (MCM complex). It combines with ATP and DNA. The biological processes in which it is involved are the cell cycle and DNA replication initiation. When comparing the gEECs group, ANXA11 expression was upregulated in the LPS-treated gEECs group. ANXA11 is involved in the process of mesosome formation and cytokinesis. The biological processes in which it is involved are the cell cycle and cell division. The changes of these proteins are closely related to the changes of endometrial function.

The Possible Role of Significantly Different Proteins in Hormones
When comparing the gEEC group, CYB5R3 expression was upregulated in the LPStreated gEECs group. The biological processes in which CYB5R3 is involved are cholesterol biosynthesis [9,10]. When comparing the gEECs group, CSN1S2 expression was downregulated in the LPS-treated gEECs group. Casocidin-I inhibits the growth of E. coli and S. carnosus. The biological processes in which it is involved are the defense response to bacterium, response to dehydroepiandrosterone, response to estradiol, response to growth hormone and response to progesterone.

The Possible Role of Significantly Different Proteins in the Immune System
When comparing the gEECs group, the expression of RBM14 in LPS-treated gEECs group was down-regulated. RBM14 may act as a nuclear receptor coactivator, promoting transcription through other coactivators. It regulates centrosome biogenesis by inhibiting the formation of abnormal centrosomal-protein complexes in the cytoplasm, thus maintaining the integrity of mitotic spindles. It is involved in the regulation of innate immune response mediated by the DNA virus by assembling HDP-RNP complex. HDP-RNP complex is the platform for IRF3 phosphorylation. This suggests that inflammation is closely related to protein phosphorylation.

The Possible Role of Significantly Different Proteins in Stress
When comparing the gEECs group, UFL1 expression was upregulated in the LPStreated gEECs group. It participates in response to endoplasmic reticulum stress. It participates in ufmylation-dependent reticulophagy and inhibits the unfolded protein response (UPR) via ERN1/IRE1-alpha. When comparing the gEECs group, VBP1 expression was upregulated in the LPS-treated gEECs group. VBP1 binds specifically to cytosolic chaperonin (c-CPN) and transfers target proteins to it. The biological process in which it is involved is protein folding. When comparing the gEECs group, PSMB1 expression was upregulated in the LPS-treated gEECs group. The noncatalytic component of the 20S core proteasome complex is involved in the proteolytic degradation of most intracellular proteins.

The Possible Role of Significantly Different Proteins in Energy Supply
When comparing the gEECs group, the expression of NDUFA9 in the LPS-treated gEECs group was up-regulated. NDUFA9 is a subsidiary subunit of mitochondrial membrane respiratory chain NADH dehydrogenase. Compared with the gEECs group, MDH2 expression was upregulated in the LPS-treated gEECs group. The biological process in which MDH2is involved is the tricarboxylic acid cycle.

Goat Endometrial Epithelial Cells (gEECs) Challenged with E. coli LPS
Cultured gEECs were challenged with E. coli LPS (Dulbecco's Modified Eagle Medium, DMEM/F12, Hyclone, Logan, UT, USA) with concentrations in the range of those previ-ously reported in vitro studies [11,12]. LPS (0 and 3 µg/mL LPS) was applied to the gEECs for 24 h with three biological repeats per sample [12].

Extraction of Cell Samples
Cellular pellets were solubilized in a buffer with protease inhibitors [12,13]. Briefly, the cells were washed with PBS buffer for 5 min each time, split with 20 µL cleavage buffer, scraped off, and placed on ice [13].
Afterward, the cells were centrifuged at room temperature at 10,000 rpm for 10 min. The precipitate was discarded [13,14]. Protein quantification was performed using a bicinchoninic acid (BCA) Protein Assay quantification kit. All steps are carried out according to the instructions for the reagent [14].

Preparation of Colloidal Particles
The samples were prepared for short-range SDS-PAGE (12%) [14,15]. Gels were stained in colloidal Coomassie blue. After gel staining, each lane was cut into small pieces and subjected to in-gel digestion with SimplyBlue Safe Stain (Invitrogen, Carlsbad, CA, USA) [15]. All the steps were operated in the specified temperature range.

Digestion in Protein Gel
A gel containing 100 µg of proteins was mixed with dithiothreitol (final concentration of 10 mM) and kept at 60 • C for 70 min [15,16]. After the sample was cooled, iodine acetamide with a final concentration of 66 mM was added and reacted away from light at room temperature for 40 min [16].
After that, 200 µL NH 4 HCO 3 solution (50 mM) was added to the filter and centrifuged at 14,000× g for 25 min. Finally, 1.25 µg of trypsin was added and the mixture incubated at 40 • C overnight [16,17]. The digestion procedure was stopped by adding formic acid (10%), and digested peptides were collected by centrifuging at 14,000× g for 15 min. After desalting with the C18 tips, the collected peptides were vacuum-dried [17,18]. After trypsin digestion, Ziptip C18 micropipette tips (Millipore) were used to purify the peptides prior to adding 0.1% formic acid for LC-MS/MS analysis.

Data Processing
We analyzed all LC-MS/MS raw data files using Proteome Discoverer 2.2 software (version 2.2; Thermo Fisher Scientific). Difference screening criteria: Ratio > 1.5 or <0.67, p-value < 0.05 [9,19]. Due to insufficient protein annotations for goat species, we searched for data in a "Ruminantia" database from UniProt (protein database: ruminantia-filtered-reviewed_yes.fasta) [9,20] A maximum of two missed cleavages was allowed. The maximum false discovery rate for peptide and protein was specified as 0.01; the LFQ (Label-Free Quantitation) was enabled with the LFQ minimum ratio count to 1. Regression Settings: Regression Model, Non-linear Regression, Parameter Tuning, Coarse. The other settings were kept as default. GraphPad Prism v7.0 was used with all data pre-tested for normality [9,20]. Additionally, the values are presented as mean ± SD. Difference between an experimental group and a control was analyzed with Student's t-test or one-way ANOVA [9,18]. The dataset identifier IPX0004443000. https://www.iprox.cn/page/PSV023.html;?url=1677679444384dvFy (accessed on 5 March 2023) [3][4][5]. Passport: OkPc. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange.org (accessed on 5 March 2023)) via the iProX partner repository with the dataset identifier IPX0004443000 [12][13][14].  [19,20]. The GO mapping and annotation (http://geneontology.org/ (accessed on 22 July 2021)) results were plotted using Cluster 3.0 and Blast2 GO (version 3.3.5). The GO annotation results were plotted using R scripts. Functional annotation was performed using the Gene Ontology (GO) database (http://www.geneontology.org (accessed on 22 July 2021)) and included the cellular component, molecular function, and biological process. Briefly, the identified protein ID was converted to UniProt ID and then mapped to GO IDs by protein ID. The retrieved credibility is ≥95%, false positive is <5% in the database; difference ratio of proteins is ≥1.5-fold and p-value is ≤0.05. The enrichment analysis revealed 20 significant GO terms by hypergeometric test (p < 0.05). The FASTA protein sequences of differentially expressed proteins (DEPs) were blasted against the online GO database (http://geneontology.org/ (accessed on 22 July 2021)) to retrieve their GO annotations and were subsequently mapped to pathways in KEGG. GO and KEGG pathway enrichment analyses were applied based on Fisher's exact test. The LFQ was selected for the quantification. The hypothesis was performed using the t-Test, and the associated p-values were adjusted based on Benjamini-Hochberg's correction (FDR < 0.05). Briefly, the Benjamini-Hochberg correction for multiple tests was further applied to adjust the derived p-values. Only functional categories and pathways with p-values under a threshold of 0.05 were considered significant. The other settings were kept as default [20,21]. The analysis showed the 20 most relevant pathways sorted by p-value. The pathway enrichment analysis was performed using the database for annotation, visualization and integrated discovery.

Hierarchical Clustering from Differentially Expressed Proteins
The related protein data were used to perform hierarchical clustering analysis [20,22]. For this purpose, Cluster 3.0 (http://bonsai.hgc.jp/~mdehoon/software/cluster/software. htm (accessed on 22 July 2021)), Java TreeView software (http://jtreeview.sourceforge.net (accessed on 22 July 2021)) and R scripts were used. The Euclidean distance algorithm for similarity measurement and the average linkage clustering algorithm (clustering using the centroids of the observations) were selected during hierarchical clustering [22,23].

Protein-protein Interaction (PPI) Networks from Differentially Expressed Proteins
The PPI information for the studied proteins used the gene symbols and STRING software, version 11 [23]. The PPI information for the studied proteins was retrieved from the IntAct molecular interaction database (http://www.ebi.ac.uk/intact/ (accessed on 22 July 2021)) using the gene symbols and STRING (http://string-db.org/ (accessed on 22 July 2021)) software. The difference ratio of proteins is ≥1.5-fold and p-value is ≤0.05; identified proteins must be redundant by all artificial. The minimum interaction score was set to medium at 0.40 as the confidence score. The primary default setting was used for the analysis. The results were downloaded in XGMML format and imported into Cytoscape5 software (http://www.cytoscape.org/ (accessed on 22 July 2021), version 3.2.1) to visualize and further analyze the functional PPI networks [23,24].

Confocal Microscopy
Cells were seeded in confocal dishes until growth was >80%. The cell culture medium was removed by aspiration and cells were washed once with PBS. The treatment was performed as described. The cells were fixed by 4% paraformaldehyde for 20 min, then permeabilized with 0.5% triton-x diluted for 20 min. The cells were blocked with 10% FBS in 1× PBS containing 0.5% Tween-20 [14,15].

Conclusions
One of the most popular techniques for finding potential biomarkers for many diseases in recent years is quantitative methods of mass, which include ultra-sensitive mass spectrometry. A total of 1180 proteins were identified in the goat Endometrial Epithelial Cells and LPS-treated goat Endometrial Epithelial Cell groups, of which 313 differential proteins were accurately screened. Compared with the goat Endometrial Epithelial Cells group, the inflammatory response, endoplasmic reticulum stress, apoptosis and mitochondrial function were activated or inhibited from differentially expressed proteins in the LPS-treated goat Endometrial Epithelial Cells group. The results of differentially expressed proteins showed that several signaling pathways related to biological processes or molecular function of proteins were upregulated or downregulated. The results were independently verified by WB, TEM and IF techniques, and the results were consistent with the proteomic data. Additionally, some of these proteins can be used as biomarker proteins to predict diseases. Our team will continue to study the relevant molecular mechanisms in depth. All findings reported here may provide new ideas and directions to improve our understanding of the biological mechanism of endometritis.  Data Availability Statement: The mass spectrometry proteomics data have been deposited to the Pro-teomeXchange Consortium (http://proteomecentral.proteomexchange.org (accessed on 5 March 2023)) via the iProX partner repository with the dataset identifier IPX0004443000, https://www.iprox. cn/page/MSV022.html (accessed on 5 March 2023). "The datasets presented in this study can be found in online repositories. The name of the repository and accession number can be found below: iProX (https://www.iprox.cn/ (accessed on 5 March 2023)); IPX0004443000." The datasets generated and/or analysed during the current study are available in the (Uniprot) repository, (COL1A1, Protein ID: P02453; SERPINE1, Protein ID: P13909; KNG1, Protein ID: P01044). The data that supports the findings of this study are available in the supplementary material of this article. "Not applicable" for studies not involving humans or animals.